One of the key points made by Mr. Ganesan was that, unlike other industry sectors, many organizations in the health and human service field have traditionally been “data poor.” For the executive teams of these organizations, in an attempt to dispel the confusion of working in a data poor environment, they are now producing so much data that they are once again limited in their ability to make decisions. As the saying goes, they are data-rich and information poor.

Mr. Ganesan discussed this phenomenon and the need to develop a data strategy. He noted, “This is easy to do, but it’s also very easy not to do. It is important to remember that data is a corporate asset, and if it’s that important, we need a strategy for how we handle that data.” But, to do this, executive teams first need to assess how “data mature” their organization currently is. There are four stages of data maturity:

Undisciplined – There is data, but the lack of an organization-wide tech strategy leads to a lot of data redundancy and a waste of both technology and human resources.

Reactive – There is still poor organization-wide buy-in, but there are better quality technical employees with a stronger emphasis on the process and the data produced.

Proactive – Management has recognized data as a “corporate asset” and data management initiatives have become common at all levels of the organization.

Governed – Data models are used to capture the business meaning and technical details of all corporate data elements, and the organization has adopted a “zero defects” data management policy.

Most areas of the organization are focused on data management initiatives

Process

More standardization

Emergence of Metrics

Focus on problem prevention vs correction

Technology

Technology providers become strategic partners

Emergence of corporate data management group

Risk and Reward

Risk – Medium/Low; Reward – Medium/High

Data Maturity – Governed

People

Executive support for data quality management

Operational data management group

“Zero Defect” policies for data management

Process

Processes for data consistency and accuracy

Impact analysis for new initiatives

Technology

Data models capture the business meaning and technical details of all corporate data elements

Standard metadata and rules

Results of data quality audits are continuously inspected,

Risk and Reward

Risk: Low; Rewards: High;

Once you have assessed you organization’s current data maturity and use of analytics, you can build a strategy that moves you closer to the “governed” level of data management. Mr Ganesan noted – “Data informed decision making means you must take action. You have to use the data, and you must empower the staff to do so also. Think of data, and using data as the driver in the behavior modification business. [Organizations] leverage data to create success, dramatically shortening cycles, and the competition is now someone you don’t know yet. Technology enabled businesses are the more likely to be successful in this environment.”

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